How to predict a time series set with statsmodels Holt-Winters

Question

I have a set of data from January 2012 to December 2014 that show some trend and seasonality. I want to make a prediction for the next 2 years (from January 2015 to December 2017), by using the Holt-Winters method from statsmodels. The data set is the following one:

date,Data
Jan-12,153046
Feb-12,161874
Mar-12,226134
Apr-12,171871
May-12,191416
Jun-12,230926
Jul-12,147518
Aug-12,107449
Sep-12,170645
Oct-12,176492
Nov-12,180005
Dec-12,193372
Jan-13,156846
Feb-13,168893
Mar-13,231103
Apr-13,187390
May-13,191702
Jun-13,252216
Jul-13,175392
Aug-13,150390
Sep-13,148750
Oct-13,173798
Nov-13,171611
Dec-13,165390
Jan-14,155079
Feb-14,172438
Mar-14,225818
Apr-14,188195
May-14,193948
Jun-14,230964
Jul-14,172225
Aug-14,129257
Sep-14,173443
Oct-14,188987
Nov-14,172731
Dec-14,211194

Which looks like follows:

I'm trying to build the Holt-Winters model, in order to improve the prediction performance of the past data (it means, a new graph where I can see if my parameters perform a good prediction of the past) and later on forecast the next years. I made the prediction with the following code, but I'm not able to do the forecast.

# Data loading
data = pd.read_csv('setpoints.csv', parse_dates=['date'], index_col=['date'])
df_data = pd.DataFrame(datos_matric, columns=['Data'])

df_data['Data'].index.freq = 'MS'
train, test = df_data['Data'], df_data['Data']
model = ExponentialSmoothing(train, trend='add', seasonal='add', seasonal_periods=12).fit()
period = ['Jan-12', 'Dec-14']
pred = model.predict(start=period[0], end=period[1])


df_data['Data'].plot(label='Train')
test.plot(label='Test')
pred.plot(label='Holt-Winters')
plt.legend(loc='best')
plt.show()

Which looks like:

Does anyone now how to forecast it?

Answer 1

I think you are making a misconception here. You shouldnt use the same data for train and test . The test data are datapoints which your model "has not seen yet". This way you can test how well your model is performing. So I used the last three months of your data as test .

As for the prediction, we can use different start and end points.

Also notice I used mul as seasonal component , which performs better on your data:

# read in data and convert date column to MS frequency
df = pd.read_csv(data)
df['date'] = pd.to_datetime(df['date'], format='%b-%y')
df = df.set_index('date').asfreq('MS')

# split data in train, test
train = df.loc[:'2014-09-01']
test = df.loc['2014-10-01':]

# train model and predict
model = ExponentialSmoothing(train, seasonal='mul', seasonal_periods=12).fit()
#model = ExponentialSmoothing(train, trend='add', seasonal='add', seasonal_periods=12).fit()
pred_test = model.predict(start='2014-10-01', end='2014-12-01')
pred_forecast = model.predict(start='2015-01-01', end='2017-12-01')

# plot data and prediction
df.plot(figsize=(15,9), label='Train')
pred_test.plot(label='Test')
pred_forecast.plot(label='Forecast')
plt.legend()
plt.show()
plt.savefig('figure.png')